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In the post epidemic era, the movement and distribution of pathogenic airflow and droplets produced by cough in the building space have been widely studied. Due to the limitations of research methods, there are few detailed research data on the temporal and spatial distribution of boundary conditions during cough, which is the basis of research and the key boundary conditions of computer simulation. Previous experiments have obtained cough airflow velocity distribution away from the mouth. This study aims to infer detailed data at mouth for CFD boundary conditions based on these experimental data. This is the first part of the research. Based on experiments, the types of parameters contained in the boundary conditions near the mouth during coughing are discussed. The main parameters are determined, including the maximum velocity of the mouth air flow, and the distribution function of the ejected air flow, among others, and the approximate value range. Different parameter combinations are used as boundary conditions for simulation, and with various combinations, database of conditions are obtained. Preliminary machine learning is performed on these databases, and boundary condition data consistent with experimental results are inferred. The study demonstrates that when the velocity distribution of the air flow at mouth satisfies the normal distribution function on the central vertical two-dimensional profile, the maximum velocity of the mouth air flow is 15m/s. Part 2 will use the complex neural network model to fit and infer more accurate boundary condition. The findings of this study can provide more accurate boundary conditions for simulating pathogenic airflow, as well as a supplementary database for epidemiological research.

In the post-epidemic era, the trajectory of pathogenic airflows and droplets generated by coughing have been widely studied. However, owing to the limitations of measurement methods, there is a lack of detailed data on their spatiotemporal distribution at the mouth during coughing, which are the basis of research and the critical boundary conditions for computational simulation. Previous experiments have determined the velocity distribution of coughing airflow in spaces located far from the mouth. This study aims to collect detailed data at the mouth for use as the Computational Fluid Dynamics (CFD) boundary conditions from the experimental data. In Part I of this study, the critical parameters that describe the boundary conditions at the mouth for CFD simulation were obtained. Based on these parameters, this part infers the detailed temporal and spatial distribution velocity data of the coughing airflow at the mouth using a neural network. We performed CFD simulation on the prediction results with V=10.76 and M=4, and got FAC2=0.56 compared with the experimental values. The results obtained provided a generic detailed boundary condition for coughing airflow at the mouth and appropriate machine-learning parameters. This study can provide more accurate boundary conditions for simulating the propagation of pathogenic airflow and a supplementary database for epidemic prevention research.

We examine the impact of digital transformation on a firm's ESG performance using data from listed firms in China. Leveraging the recent advances in textual analysis, we quantify the extent of a firm's digital transformation. The results show that digital transformation enhances a firm's ESG performance. Moreover, results remain robust after addressing endogeneity problems and several robustness tests. In addition, we validate potential mechanisms that the digital transformation mainly improves the ESG performance of firms by improving the level of environmental performance, corporate social responsibility, and corporate governance. To examine the economic consequence, we find that digital transformation gains more government subsidies and analysts' attention increasing the management's positive tone at the earnings communication conferences by improving the firm's ESG performance. Heterogeneity analysis suggests that the effect of digital transformation on ESG performance is stronger for firms in severely polluting industries and executives with information technology experience. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD "seed" genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology.

Proton Exchange Membrane Fuel Cells (PEMFCs) are regarded as a promising system for power generation and energy sources in recent years. PEMFCs exhibit various advantages in comparison to other traditional energy sources especially due to their requirement for Hydrogen and air for operation while the final product is only water. Thus, the PEMFCs have received great attention for investigation of the operation under various conditions. In recent years, many researchers have done lots of mathematical modeling, but because of exact modeling absence, evolutionary optimization methods are used to adopt for parameter estimation of PEMFCs. This paper suggested a novel optimization scheme, namely, chaotic binary shark smell optimization (CBSSO), to optimize unknown parameters of PEMFC model. The CBSSO is a Meta-Heuristic algorithm which is introduced newly and has shown an appropriate ability in parameter identification and optimization cases. Moreover, the results of this proposed method are compared with some other results including typical SSO, Hybrid Artificial Bee Colony, Adaptive RNA Genetic Algorithm, Real-coded GA, Modified Particle Swarm Optimization, and Artificial Immune System. Based on the obtained results, this proposed method is very efficient in convergence and accuracy aspects compared to other famous methods. [ABSTRACT FROM AUTHOR]